Overview

Dataset statistics

Number of variables38
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory436.5 KiB
Average record size in memory304.1 B

Variable types

Numeric17
Boolean3
Categorical18

Alerts

EmployeeCount has constant value ""Constant
Over18 has constant value ""Constant
StandardHours has constant value ""Constant
Age is highly overall correlated with TotalWorkingYearsHigh correlation
Department is highly overall correlated with EducationField and 1 other fieldsHigh correlation
EducationField is highly overall correlated with DepartmentHigh correlation
Incentive is highly overall correlated with PercentSalaryHikeHigh correlation
JobLevel is highly overall correlated with JobRole and 2 other fieldsHigh correlation
JobRole is highly overall correlated with Department and 1 other fieldsHigh correlation
MaritalStatus is highly overall correlated with StockOptionLevelHigh correlation
MonthlyIncome is highly overall correlated with JobLevel and 1 other fieldsHigh correlation
PercentSalaryHike is highly overall correlated with Incentive and 1 other fieldsHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatusHigh correlation
TotalWorkingYears is highly overall correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
EmployeeNumber has unique valuesUnique
NumCompaniesWorked has 197 (13.4%) zerosZeros
TrainingTimesLastYear has 54 (3.7%) zerosZeros
YearsAtCompany has 44 (3.0%) zerosZeros
YearsInCurrentRole has 244 (16.6%) zerosZeros
YearsSinceLastPromotion has 581 (39.5%) zerosZeros
YearsWithCurrManager has 263 (17.9%) zerosZeros
Incentive has 475 (32.3%) zerosZeros
RemoteWork has 44 (3.0%) zerosZeros

Reproduction

Analysis started2024-01-14 15:43:21.217045
Analysis finished2024-01-14 15:43:54.758618
Duration33.54 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92381
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:43:54.826591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1353735
Coefficient of variation (CV)0.24741146
Kurtosis-0.40414514
Mean36.92381
Median Absolute Deviation (MAD)6
Skewness0.4132863
Sum54278
Variance83.455049
MonotonicityNot monotonic
2024-01-15T00:43:54.934693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 78
 
5.3%
34 77
 
5.2%
36 69
 
4.7%
31 69
 
4.7%
29 68
 
4.6%
32 61
 
4.1%
30 60
 
4.1%
33 58
 
3.9%
38 58
 
3.9%
40 57
 
3.9%
Other values (33) 815
55.4%
ValueCountFrequency (%)
18 8
 
0.5%
19 9
 
0.6%
20 11
 
0.7%
21 13
 
0.9%
22 16
 
1.1%
23 14
 
1.0%
24 26
1.8%
25 26
1.8%
26 39
2.7%
27 48
3.3%
ValueCountFrequency (%)
60 5
 
0.3%
59 10
0.7%
58 14
1.0%
57 4
 
0.3%
56 14
1.0%
55 22
1.5%
54 18
1.2%
53 19
1.3%
52 18
1.2%
51 19
1.3%

Attrition
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1233 
True
237 
ValueCountFrequency (%)
False 1233
83.9%
True 237
 
16.1%
2024-01-15T00:43:55.036835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Travel_Rarely
1043 
Travel_Frequently
277 
Non-Travel
150 

Length

Max length17
Median length13
Mean length13.447619
Min length10

Characters and Unicode

Total characters19768
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Rarely
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 1043
71.0%
Travel_Frequently 277
 
18.8%
Non-Travel 150
 
10.2%

Length

2024-01-15T00:43:55.120206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:55.215210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 1043
71.0%
travel_frequently 277
 
18.8%
non-travel 150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e 3067
15.5%
r 2790
14.1%
l 2790
14.1%
a 2513
12.7%
T 1470
7.4%
v 1470
7.4%
y 1320
6.7%
_ 1320
6.7%
R 1043
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15358
77.7%
Uppercase Letter 2940
 
14.9%
Connector Punctuation 1320
 
6.7%
Dash Punctuation 150
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3067
20.0%
r 2790
18.2%
l 2790
18.2%
a 2513
16.4%
v 1470
9.6%
y 1320
8.6%
n 427
 
2.8%
q 277
 
1.8%
u 277
 
1.8%
t 277
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T 1470
50.0%
R 1043
35.5%
F 277
 
9.4%
N 150
 
5.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1320
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18298
92.6%
Common 1470
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3067
16.8%
r 2790
15.2%
l 2790
15.2%
a 2513
13.7%
T 1470
8.0%
v 1470
8.0%
y 1320
7.2%
R 1043
 
5.7%
n 427
 
2.3%
F 277
 
1.5%
Other values (5) 1131
 
6.2%
Common
ValueCountFrequency (%)
_ 1320
89.8%
- 150
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3067
15.5%
r 2790
14.1%
l 2790
14.1%
a 2513
12.7%
T 1470
7.4%
v 1470
7.4%
y 1320
6.7%
_ 1320
6.7%
R 1043
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

DailyAchievement
Real number (ℝ)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.48571
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:43:55.316006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.35
Q1465
median802
Q31157
95-th percentile1424.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.5091
Coefficient of variation (CV)0.50282403
Kurtosis-1.2038228
Mean802.48571
Median Absolute Deviation (MAD)344
Skewness-0.0035185684
Sum1179654
Variance162819.59
MonotonicityNot monotonic
2024-01-15T00:43:55.429916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 6
 
0.4%
530 5
 
0.3%
408 5
 
0.3%
1329 5
 
0.3%
1082 5
 
0.3%
329 5
 
0.3%
1157 4
 
0.3%
906 4
 
0.3%
267 4
 
0.3%
334 4
 
0.3%
Other values (876) 1423
96.8%
ValueCountFrequency (%)
102 1
 
0.1%
103 1
 
0.1%
104 1
 
0.1%
105 1
 
0.1%
106 1
 
0.1%
107 1
 
0.1%
109 1
 
0.1%
111 3
0.2%
115 1
 
0.1%
116 2
0.1%
ValueCountFrequency (%)
1499 1
 
0.1%
1498 1
 
0.1%
1496 2
0.1%
1495 3
0.2%
1492 1
 
0.1%
1490 4
0.3%
1488 1
 
0.1%
1485 3
0.2%
1482 1
 
0.1%
1480 2
0.1%

Department
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Research & Development
961 
Sales
446 
Human Resources
 
63

Length

Max length22
Median length22
Mean length16.542177
Min length5

Characters and Unicode

Total characters24317
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch & Development
2nd rowResearch & Development
3rd rowSales
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development 961
65.4%
Sales 446
30.3%
Human Resources 63
 
4.3%

Length

2024-01-15T00:43:55.541110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:55.639662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
research 961
27.8%
961
27.8%
development 961
27.8%
sales 446
12.9%
human 63
 
1.8%
resources 63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
r 1024
 
4.2%
c 1024
 
4.2%
n 1024
 
4.2%
m 1024
 
4.2%
Other values (10) 7425
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18877
77.6%
Uppercase Letter 2494
 
10.3%
Space Separator 1985
 
8.2%
Other Punctuation 961
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5377
28.5%
s 1533
 
8.1%
a 1470
 
7.8%
l 1407
 
7.5%
r 1024
 
5.4%
c 1024
 
5.4%
n 1024
 
5.4%
m 1024
 
5.4%
o 1024
 
5.4%
p 961
 
5.1%
Other values (4) 3009
15.9%
Uppercase Letter
ValueCountFrequency (%)
R 1024
41.1%
D 961
38.5%
S 446
17.9%
H 63
 
2.5%
Space Separator
ValueCountFrequency (%)
1985
100.0%
Other Punctuation
ValueCountFrequency (%)
& 961
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21371
87.9%
Common 2946
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5377
25.2%
s 1533
 
7.2%
a 1470
 
6.9%
l 1407
 
6.6%
R 1024
 
4.8%
r 1024
 
4.8%
c 1024
 
4.8%
n 1024
 
4.8%
m 1024
 
4.8%
o 1024
 
4.8%
Other values (8) 5440
25.5%
Common
ValueCountFrequency (%)
1985
67.4%
& 961
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
r 1024
 
4.2%
c 1024
 
4.2%
n 1024
 
4.2%
m 1024
 
4.2%
Other values (10) 7425
30.5%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:43:55.729605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1068644
Coefficient of variation (CV)0.88189823
Kurtosis-0.2248334
Mean9.192517
Median Absolute Deviation (MAD)5
Skewness0.958118
Sum13513
Variance65.721251
MonotonicityNot monotonic
2024-01-15T00:43:55.818439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 211
14.4%
1 208
14.1%
10 86
 
5.9%
9 85
 
5.8%
3 84
 
5.7%
7 84
 
5.7%
8 80
 
5.4%
5 65
 
4.4%
4 64
 
4.4%
6 59
 
4.0%
Other values (19) 444
30.2%
ValueCountFrequency (%)
1 208
14.1%
2 211
14.4%
3 84
 
5.7%
4 64
 
4.4%
5 65
 
4.4%
6 59
 
4.0%
7 84
 
5.7%
8 80
 
5.4%
9 85
5.8%
10 86
5.9%
ValueCountFrequency (%)
29 27
1.8%
28 23
1.6%
27 12
0.8%
26 25
1.7%
25 25
1.7%
24 28
1.9%
23 27
1.8%
22 19
1.3%
21 18
1.2%
20 25
1.7%

Education
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Length

2024-01-15T00:43:55.917988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:56.023037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

EducationField
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Life Sciences
606 
Medical
464 
Marketing
159 
Technical Degree
132 
Other
82 

Length

Max length16
Median length15
Mean length10.533333
Min length5

Characters and Unicode

Total characters15484
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedical
2nd rowLife Sciences
3rd rowMarketing
4th rowMedical
5th rowLife Sciences

Common Values

ValueCountFrequency (%)
Life Sciences 606
41.2%
Medical 464
31.6%
Marketing 159
 
10.8%
Technical Degree 132
 
9.0%
Other 82
 
5.6%
Human Resources 27
 
1.8%

Length

2024-01-15T00:43:56.115388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:56.220534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
life 606
27.1%
sciences 606
27.1%
medical 464
20.8%
marketing 159
 
7.1%
technical 132
 
5.9%
degree 132
 
5.9%
other 82
 
3.7%
human 27
 
1.2%
resources 27
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 3105
20.1%
i 1967
12.7%
c 1967
12.7%
n 924
 
6.0%
a 782
 
5.1%
765
 
4.9%
s 660
 
4.3%
M 623
 
4.0%
L 606
 
3.9%
f 606
 
3.9%
Other values (16) 3479
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12484
80.6%
Uppercase Letter 2235
 
14.4%
Space Separator 765
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3105
24.9%
i 1967
15.8%
c 1967
15.8%
n 924
 
7.4%
a 782
 
6.3%
s 660
 
5.3%
f 606
 
4.9%
l 596
 
4.8%
d 464
 
3.7%
r 400
 
3.2%
Other values (7) 1013
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
M 623
27.9%
L 606
27.1%
S 606
27.1%
T 132
 
5.9%
D 132
 
5.9%
O 82
 
3.7%
H 27
 
1.2%
R 27
 
1.2%
Space Separator
ValueCountFrequency (%)
765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14719
95.1%
Common 765
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3105
21.1%
i 1967
13.4%
c 1967
13.4%
n 924
 
6.3%
a 782
 
5.3%
s 660
 
4.5%
M 623
 
4.2%
L 606
 
4.1%
f 606
 
4.1%
S 606
 
4.1%
Other values (15) 2873
19.5%
Common
ValueCountFrequency (%)
765
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3105
20.1%
i 1967
12.7%
c 1967
12.7%
n 924
 
6.0%
a 782
 
5.1%
765
 
4.9%
s 660
 
4.3%
M 623
 
4.0%
L 606
 
3.9%
f 606
 
3.9%
Other values (16) 3479
22.5%

EmployeeCount
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
1470 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1470
100.0%

Length

2024-01-15T00:43:56.323942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:56.408991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1470
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1470
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1470
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1470
100.0%

EmployeeNumber
Real number (ℝ)

UNIQUE 

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.8653
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:43:56.495904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.02433
Coefficient of variation (CV)0.58741801
Kurtosis-1.2231789
Mean1024.8653
Median Absolute Deviation (MAD)533.5
Skewness0.01657402
Sum1506552
Variance362433.3
MonotonicityNot monotonic
2024-01-15T00:43:56.611915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 1
 
0.1%
1115 1
 
0.1%
1242 1
 
0.1%
754 1
 
0.1%
1282 1
 
0.1%
867 1
 
0.1%
1924 1
 
0.1%
1394 1
 
0.1%
1611 1
 
0.1%
446 1
 
0.1%
Other values (1460) 1460
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
4 1
0.1%
5 1
0.1%
7 1
0.1%
8 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
ValueCountFrequency (%)
2068 1
0.1%
2065 1
0.1%
2064 1
0.1%
2062 1
0.1%
2061 1
0.1%
2060 1
0.1%
2057 1
0.1%
2056 1
0.1%
2055 1
0.1%
2054 1
0.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Length

2024-01-15T00:43:56.717897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:56.829960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring characters

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Male
882 
Female
588 

Length

Max length6
Median length4
Mean length4.8
Min length4

Characters and Unicode

Total characters7056
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 882
60.0%
Female 588
40.0%

Length

2024-01-15T00:43:56.922642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:57.021625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
male 882
60.0%
female 588
40.0%

Most occurring characters

ValueCountFrequency (%)
e 2058
29.2%
a 1470
20.8%
l 1470
20.8%
M 882
12.5%
F 588
 
8.3%
m 588
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5586
79.2%
Uppercase Letter 1470
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2058
36.8%
a 1470
26.3%
l 1470
26.3%
m 588
 
10.5%
Uppercase Letter
ValueCountFrequency (%)
M 882
60.0%
F 588
40.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7056
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2058
29.2%
a 1470
20.8%
l 1470
20.8%
M 882
12.5%
F 588
 
8.3%
m 588
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2058
29.2%
a 1470
20.8%
l 1470
20.8%
M 882
12.5%
F 588
 
8.3%
m 588
 
8.3%

HourlyAchievement
Real number (ℝ)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.891156
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:43:57.119850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.329428
Coefficient of variation (CV)0.30853044
Kurtosis-1.1963985
Mean65.891156
Median Absolute Deviation (MAD)18
Skewness-0.032310953
Sum96860
Variance413.28563
MonotonicityNot monotonic
2024-01-15T00:43:57.237077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 29
 
2.0%
42 28
 
1.9%
98 28
 
1.9%
84 28
 
1.9%
48 28
 
1.9%
57 27
 
1.8%
96 27
 
1.8%
79 27
 
1.8%
56 26
 
1.8%
87 26
 
1.8%
Other values (61) 1196
81.4%
ValueCountFrequency (%)
30 19
1.3%
31 15
1.0%
32 24
1.6%
33 19
1.3%
34 12
0.8%
35 18
1.2%
36 18
1.2%
37 18
1.2%
38 13
0.9%
39 17
1.2%
ValueCountFrequency (%)
100 19
1.3%
99 20
1.4%
98 28
1.9%
97 21
1.4%
96 27
1.8%
95 23
1.6%
94 22
1.5%
93 16
1.1%
92 25
1.7%
91 18
1.2%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Length

2024-01-15T00:43:57.345588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:57.762043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

JobLevel
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Length

2024-01-15T00:43:57.849051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:57.944051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

JobRole
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Sales Executive
326 
Research Scientist
292 
Laboratory Technician
259 
Manufacturing Director
145 
Healthcare Representative
131 
Other values (4)
317 

Length

Max length25
Median length21
Mean length18.070748
Min length7

Characters and Unicode

Total characters26564
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch Scientist
2nd rowResearch Scientist
3rd rowSales Executive
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive 326
22.2%
Research Scientist 292
19.9%
Laboratory Technician 259
17.6%
Manufacturing Director 145
9.9%
Healthcare Representative 131
8.9%
Manager 102
 
6.9%
Sales Representative 83
 
5.6%
Research Director 80
 
5.4%
Human Resources 52
 
3.5%

Length

2024-01-15T00:43:58.035992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:58.150087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
sales 409
14.4%
research 372
13.1%
executive 326
11.5%
scientist 292
10.3%
laboratory 259
9.1%
technician 259
9.1%
director 225
7.9%
representative 214
7.5%
manufacturing 145
 
5.1%
healthcare 131
 
4.6%
Other values (3) 206
7.3%

Most occurring characters

ValueCountFrequency (%)
e 3905
14.7%
a 2580
 
9.7%
t 2098
 
7.9%
c 2061
 
7.8%
i 2012
 
7.6%
r 1984
 
7.5%
n 1468
 
5.5%
s 1391
 
5.2%
1368
 
5.1%
o 795
 
3.0%
Other values (19) 6902
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22358
84.2%
Uppercase Letter 2838
 
10.7%
Space Separator 1368
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3905
17.5%
a 2580
11.5%
t 2098
9.4%
c 2061
9.2%
i 2012
9.0%
r 1984
8.9%
n 1468
 
6.6%
s 1391
 
6.2%
o 795
 
3.6%
h 762
 
3.4%
Other values (10) 3302
14.8%
Uppercase Letter
ValueCountFrequency (%)
S 701
24.7%
R 638
22.5%
E 326
11.5%
L 259
 
9.1%
T 259
 
9.1%
M 247
 
8.7%
D 225
 
7.9%
H 183
 
6.4%
Space Separator
ValueCountFrequency (%)
1368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25196
94.9%
Common 1368
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3905
15.5%
a 2580
10.2%
t 2098
 
8.3%
c 2061
 
8.2%
i 2012
 
8.0%
r 1984
 
7.9%
n 1468
 
5.8%
s 1391
 
5.5%
o 795
 
3.2%
h 762
 
3.0%
Other values (18) 6140
24.4%
Common
ValueCountFrequency (%)
1368
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3905
14.7%
a 2580
 
9.7%
t 2098
 
7.9%
c 2061
 
7.8%
i 2012
 
7.6%
r 1984
 
7.5%
n 1468
 
5.5%
s 1391
 
5.2%
1368
 
5.1%
o 795
 
3.0%
Other values (19) 6902
26.0%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Length

2024-01-15T00:43:58.271060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:58.366003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring characters

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

MaritalStatus
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Married
673 
Single
470 
Divorced
327 

Length

Max length8
Median length7
Mean length6.9027211
Min length6

Characters and Unicode

Total characters10147
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowDivorced
3rd rowSingle
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 673
45.8%
Single 470
32.0%
Divorced 327
22.2%

Length

2024-01-15T00:43:58.466944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:58.575986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
married 673
45.8%
single 470
32.0%
divorced 327
22.2%

Most occurring characters

ValueCountFrequency (%)
r 1673
16.5%
i 1470
14.5%
e 1470
14.5%
d 1000
9.9%
M 673
6.6%
a 673
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8677
85.5%
Uppercase Letter 1470
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1673
19.3%
i 1470
16.9%
e 1470
16.9%
d 1000
11.5%
a 673
7.8%
n 470
 
5.4%
g 470
 
5.4%
l 470
 
5.4%
v 327
 
3.8%
o 327
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
M 673
45.8%
S 470
32.0%
D 327
22.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 10147
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1673
16.5%
i 1470
14.5%
e 1470
14.5%
d 1000
9.9%
M 673
6.6%
a 673
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1673
16.5%
i 1470
14.5%
e 1470
14.5%
d 1000
9.9%
M 673
6.6%
a 673
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

MonthlyIncome
Real number (ℝ)

HIGH CORRELATION 

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:43:58.679387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2024-01-15T00:43:58.791837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2342 4
 
0.3%
2451 3
 
0.2%
2741 3
 
0.2%
6347 3
 
0.2%
6142 3
 
0.2%
2610 3
 
0.2%
3452 3
 
0.2%
2380 3
 
0.2%
2404 3
 
0.2%
5562 3
 
0.2%
Other values (1339) 1439
97.9%
ValueCountFrequency (%)
1009 1
0.1%
1051 1
0.1%
1052 1
0.1%
1081 1
0.1%
1091 1
0.1%
1102 1
0.1%
1118 1
0.1%
1129 1
0.1%
1200 1
0.1%
1223 1
0.1%
ValueCountFrequency (%)
19999 1
0.1%
19973 1
0.1%
19943 1
0.1%
19926 1
0.1%
19859 1
0.1%
19847 1
0.1%
19845 1
0.1%
19833 1
0.1%
19740 1
0.1%
19717 1
0.1%

MonthlyAchievement
Real number (ℝ)

Distinct1427
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.103
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:43:58.915351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786
Coefficient of variation (CV)0.4972916
Kurtosis-1.2149561
Mean14313.103
Median Absolute Deviation (MAD)6206.5
Skewness0.018577808
Sum21040262
Variance50662878
MonotonicityNot monotonic
2024-01-15T00:43:59.031901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9150 3
 
0.2%
4223 3
 
0.2%
22074 2
 
0.1%
21981 2
 
0.1%
6670 2
 
0.1%
20364 2
 
0.1%
6881 2
 
0.1%
12858 2
 
0.1%
6069 2
 
0.1%
4156 2
 
0.1%
Other values (1417) 1448
98.5%
ValueCountFrequency (%)
2094 1
0.1%
2097 1
0.1%
2104 1
0.1%
2112 1
0.1%
2122 1
0.1%
2125 2
0.1%
2137 1
0.1%
2227 1
0.1%
2243 1
0.1%
2253 1
0.1%
ValueCountFrequency (%)
26999 1
0.1%
26997 1
0.1%
26968 1
0.1%
26959 1
0.1%
26956 1
0.1%
26933 1
0.1%
26914 1
0.1%
26897 1
0.1%
26894 1
0.1%
26862 1
0.1%

NumCompaniesWorked
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6931973
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:43:59.123030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009
Coefficient of variation (CV)0.92752545
Kurtosis0.010213817
Mean2.6931973
Median Absolute Deviation (MAD)1
Skewness1.0264711
Sum3959
Variance6.240049
MonotonicityNot monotonic
2024-01-15T00:43:59.196054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 521
35.4%
0 197
 
13.4%
3 159
 
10.8%
2 146
 
9.9%
4 139
 
9.5%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
9 52
 
3.5%
8 49
 
3.3%
ValueCountFrequency (%)
0 197
 
13.4%
1 521
35.4%
2 146
 
9.9%
3 159
 
10.8%
4 139
 
9.5%
5 63
 
4.3%
6 70
 
4.8%
7 74
 
5.0%
8 49
 
3.3%
9 52
 
3.5%
ValueCountFrequency (%)
9 52
 
3.5%
8 49
 
3.3%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
4 139
 
9.5%
3 159
 
10.8%
2 146
 
9.9%
1 521
35.4%
0 197
 
13.4%

Over18
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1470 
ValueCountFrequency (%)
True 1470
100.0%
2024-01-15T00:43:59.282066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

OverTime
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1054 
True
416 
ValueCountFrequency (%)
False 1054
71.7%
True 416
 
28.3%
2024-01-15T00:43:59.355600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2095238
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:43:59.429787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16
median8
Q312
95-th percentile16
Maximum19
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6599377
Coefficient of variation (CV)0.39740792
Kurtosis-0.30059822
Mean9.2095238
Median Absolute Deviation (MAD)2
Skewness0.82112798
Sum13538
Variance13.395144
MonotonicityNot monotonic
2024-01-15T00:43:59.507297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 210
14.3%
7 209
14.2%
8 201
13.7%
6 198
13.5%
9 101
6.9%
12 89
6.1%
11 82
 
5.6%
10 78
 
5.3%
13 76
 
5.2%
16 56
 
3.8%
Other values (5) 170
11.6%
ValueCountFrequency (%)
5 210
14.3%
6 198
13.5%
7 209
14.2%
8 201
13.7%
9 101
6.9%
10 78
 
5.3%
11 82
 
5.6%
12 89
6.1%
13 76
 
5.2%
14 55
 
3.7%
ValueCountFrequency (%)
19 18
 
1.2%
18 21
 
1.4%
17 28
 
1.9%
16 56
3.8%
15 48
3.3%
14 55
3.7%
13 76
5.2%
12 89
6.1%
11 82
5.6%
10 78
5.3%

PerformanceRating
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
495 
2
436 
4
276 
1
263 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 495
33.7%
2 436
29.7%
4 276
18.8%
1 263
17.9%

Length

2024-01-15T00:43:59.600833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:59.696852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3 495
33.7%
2 436
29.7%
4 276
18.8%
1 263
17.9%

Most occurring characters

ValueCountFrequency (%)
3 495
33.7%
2 436
29.7%
4 276
18.8%
1 263
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 495
33.7%
2 436
29.7%
4 276
18.8%
1 263
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 495
33.7%
2 436
29.7%
4 276
18.8%
1 263
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 495
33.7%
2 436
29.7%
4 276
18.8%
1 263
17.9%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Length

2024-01-15T00:43:59.785061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:43:59.881092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring characters

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

StandardHours
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
80
1470 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2940
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80
2nd row80
3rd row80
4th row80
5th row80

Common Values

ValueCountFrequency (%)
80 1470
100.0%

Length

2024-01-15T00:43:59.974157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:44:00.063179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
80 1470
100.0%

Most occurring characters

ValueCountFrequency (%)
8 1470
50.0%
0 1470
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 1470
50.0%
0 1470
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 1470
50.0%
0 1470
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 1470
50.0%
0 1470
50.0%

StockOptionLevel
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Length

2024-01-15T00:44:00.139254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:44:00.234441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

TotalWorkingYears
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279592
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:44:00.331840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7807817
Coefficient of variation (CV)0.68981057
Kurtosis0.91826954
Mean11.279592
Median Absolute Deviation (MAD)4
Skewness1.1171719
Sum16581
Variance60.540563
MonotonicityNot monotonic
2024-01-15T00:44:00.436938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 202
 
13.7%
6 125
 
8.5%
8 103
 
7.0%
9 96
 
6.5%
5 88
 
6.0%
7 81
 
5.5%
1 81
 
5.5%
4 63
 
4.3%
12 48
 
3.3%
3 42
 
2.9%
Other values (30) 541
36.8%
ValueCountFrequency (%)
0 11
 
0.7%
1 81
5.5%
2 31
 
2.1%
3 42
 
2.9%
4 63
4.3%
5 88
6.0%
6 125
8.5%
7 81
5.5%
8 103
7.0%
9 96
6.5%
ValueCountFrequency (%)
40 2
 
0.1%
38 1
 
0.1%
37 4
0.3%
36 6
0.4%
35 3
 
0.2%
34 5
0.3%
33 7
0.5%
32 9
0.6%
31 9
0.6%
30 7
0.5%

TrainingTimesLastYear
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:44:00.529939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2024-01-15T00:44:00.599856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
1 71
 
4.8%
6 65
 
4.4%
0 54
 
3.7%
ValueCountFrequency (%)
0 54
 
3.7%
1 71
 
4.8%
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
6 65
 
4.4%
ValueCountFrequency (%)
6 65
 
4.4%
5 119
 
8.1%
4 123
 
8.4%
3 491
33.4%
2 547
37.2%
1 71
 
4.8%
0 54
 
3.7%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Length

2024-01-15T00:44:00.693305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:44:00.784232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

YearsAtCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:44:00.880073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1265252
Coefficient of variation (CV)0.87419841
Kurtosis3.9355088
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7645295
Sum10302
Variance37.53431
MonotonicityNot monotonic
2024-01-15T00:44:00.983143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 196
13.3%
1 171
11.6%
3 128
8.7%
2 127
8.6%
10 120
8.2%
4 110
 
7.5%
7 90
 
6.1%
9 82
 
5.6%
8 80
 
5.4%
6 76
 
5.2%
Other values (27) 290
19.7%
ValueCountFrequency (%)
0 44
 
3.0%
1 171
11.6%
2 127
8.6%
3 128
8.7%
4 110
7.5%
5 196
13.3%
6 76
 
5.2%
7 90
6.1%
8 80
5.4%
9 82
5.6%
ValueCountFrequency (%)
40 1
 
0.1%
37 1
 
0.1%
36 2
 
0.1%
34 1
 
0.1%
33 5
0.3%
32 3
0.2%
31 3
0.2%
30 1
 
0.1%
29 2
 
0.1%
27 2
 
0.1%

YearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2292517
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:44:01.080176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137
Coefficient of variation (CV)0.85668513
Kurtosis0.47742077
Mean4.2292517
Median Absolute Deviation (MAD)3
Skewness0.91736316
Sum6217
Variance13.127122
MonotonicityNot monotonic
2024-01-15T00:44:01.164324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 372
25.3%
0 244
16.6%
7 222
15.1%
3 135
 
9.2%
4 104
 
7.1%
8 89
 
6.1%
9 67
 
4.6%
1 57
 
3.9%
6 37
 
2.5%
5 36
 
2.4%
Other values (9) 107
 
7.3%
ValueCountFrequency (%)
0 244
16.6%
1 57
 
3.9%
2 372
25.3%
3 135
 
9.2%
4 104
 
7.1%
5 36
 
2.4%
6 37
 
2.5%
7 222
15.1%
8 89
 
6.1%
9 67
 
4.6%
ValueCountFrequency (%)
18 2
 
0.1%
17 4
 
0.3%
16 7
 
0.5%
15 8
 
0.5%
14 11
 
0.7%
13 14
 
1.0%
12 10
 
0.7%
11 22
 
1.5%
10 29
2.0%
9 67
4.6%

YearsSinceLastPromotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:44:01.254350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2224303
Coefficient of variation (CV)1.4729392
Kurtosis3.6126731
Mean2.1877551
Median Absolute Deviation (MAD)1
Skewness1.98429
Sum3216
Variance10.384057
MonotonicityNot monotonic
2024-01-15T00:44:01.338885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
7 76
 
5.2%
4 61
 
4.1%
3 52
 
3.5%
5 45
 
3.1%
6 32
 
2.2%
11 24
 
1.6%
8 18
 
1.2%
Other values (6) 65
 
4.4%
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
3 52
 
3.5%
4 61
 
4.1%
5 45
 
3.1%
6 32
 
2.2%
7 76
 
5.2%
8 18
 
1.2%
9 17
 
1.2%
ValueCountFrequency (%)
15 13
 
0.9%
14 9
 
0.6%
13 10
 
0.7%
12 10
 
0.7%
11 24
 
1.6%
10 6
 
0.4%
9 17
 
1.2%
8 18
 
1.2%
7 76
5.2%
6 32
2.2%

YearsWithCurrManager
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:44:01.431564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5681361
Coefficient of variation (CV)0.86539517
Kurtosis0.17105808
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83345099
Sum6061
Variance12.731595
MonotonicityNot monotonic
2024-01-15T00:44:01.520150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 344
23.4%
0 263
17.9%
7 216
14.7%
3 142
9.7%
8 107
 
7.3%
4 98
 
6.7%
1 76
 
5.2%
9 64
 
4.4%
5 31
 
2.1%
6 29
 
2.0%
Other values (8) 100
 
6.8%
ValueCountFrequency (%)
0 263
17.9%
1 76
 
5.2%
2 344
23.4%
3 142
9.7%
4 98
 
6.7%
5 31
 
2.1%
6 29
 
2.0%
7 216
14.7%
8 107
 
7.3%
9 64
 
4.4%
ValueCountFrequency (%)
17 7
 
0.5%
16 2
 
0.1%
15 5
 
0.3%
14 5
 
0.3%
13 14
 
1.0%
12 18
 
1.2%
11 22
 
1.5%
10 27
 
1.8%
9 64
4.4%
8 107
7.3%

HowToEmploy
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
New_graduate_recruitment
843 
agent_A
167 
agent_C
151 
intern
133 
agent_B
109 

Length

Max length24
Median length24
Mean length17.068707
Min length6

Characters and Unicode

Total characters25091
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowintern
2nd rowintern
3rd rowagent_A
4th rowNew_graduate_recruitment
5th rowNew_graduate_recruitment

Common Values

ValueCountFrequency (%)
New_graduate_recruitment 843
57.3%
agent_A 167
 
11.4%
agent_C 151
 
10.3%
intern 133
 
9.0%
agent_B 109
 
7.4%
direct_recruting 67
 
4.6%

Length

2024-01-15T00:44:01.624293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-15T00:44:01.737969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
new_graduate_recruitment 843
57.3%
agent_a 167
 
11.4%
agent_c 151
 
10.3%
intern 133
 
9.0%
agent_b 109
 
7.4%
direct_recruting 67
 
4.6%

Most occurring characters

ValueCountFrequency (%)
e 4066
16.2%
t 3223
12.8%
r 2863
11.4%
_ 2180
8.7%
a 2113
8.4%
u 1753
7.0%
n 1603
 
6.4%
g 1337
 
5.3%
i 1110
 
4.4%
c 977
 
3.9%
Other values (7) 3866
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21641
86.3%
Connector Punctuation 2180
 
8.7%
Uppercase Letter 1270
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4066
18.8%
t 3223
14.9%
r 2863
13.2%
a 2113
9.8%
u 1753
8.1%
n 1603
 
7.4%
g 1337
 
6.2%
i 1110
 
5.1%
c 977
 
4.5%
d 910
 
4.2%
Other values (2) 1686
7.8%
Uppercase Letter
ValueCountFrequency (%)
N 843
66.4%
A 167
 
13.1%
C 151
 
11.9%
B 109
 
8.6%
Connector Punctuation
ValueCountFrequency (%)
_ 2180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22911
91.3%
Common 2180
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4066
17.7%
t 3223
14.1%
r 2863
12.5%
a 2113
9.2%
u 1753
7.7%
n 1603
 
7.0%
g 1337
 
5.8%
i 1110
 
4.8%
c 977
 
4.3%
d 910
 
4.0%
Other values (6) 2956
12.9%
Common
ValueCountFrequency (%)
_ 2180
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25091
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4066
16.2%
t 3223
12.8%
r 2863
11.4%
_ 2180
8.7%
a 2113
8.4%
u 1753
7.0%
n 1603
 
6.4%
g 1337
 
5.3%
i 1110
 
4.4%
c 977
 
3.9%
Other values (7) 3866
15.4%

Incentive
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct840
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1183.3864
Minimum0
Maximum8584
Zeros475
Zeros (%)32.3%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:44:01.856799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median727.5
Q31817
95-th percentile4194.4
Maximum8584
Range8584
Interquartile range (IQR)1817

Descriptive statistics

Standard deviation1429.6875
Coefficient of variation (CV)1.2081325
Kurtosis2.8955703
Mean1183.3864
Median Absolute Deviation (MAD)727.5
Skewness1.6398126
Sum1739578
Variance2044006.4
MonotonicityNot monotonic
2024-01-15T00:44:01.968875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
32.3%
513 4
 
0.3%
1225 4
 
0.3%
355 3
 
0.2%
306 3
 
0.2%
1129 3
 
0.2%
1171 3
 
0.2%
119 3
 
0.2%
1014 3
 
0.2%
1293 3
 
0.2%
Other values (830) 966
65.7%
ValueCountFrequency (%)
0 475
32.3%
34 1
 
0.1%
36 1
 
0.1%
63 1
 
0.1%
67 1
 
0.1%
69 1
 
0.1%
70 1
 
0.1%
72 1
 
0.1%
88 1
 
0.1%
93 1
 
0.1%
ValueCountFrequency (%)
8584 1
0.1%
8016 1
0.1%
7049 1
0.1%
7005 1
0.1%
6788 1
0.1%
6749 1
0.1%
6634 1
0.1%
6618 1
0.1%
6595 1
0.1%
6594 1
0.1%

RemoteWork
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8244898
Minimum0
Maximum5
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-01-15T00:44:02.054225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2295207
Coefficient of variation (CV)0.43530718
Kurtosis-0.51263302
Mean2.8244898
Median Absolute Deviation (MAD)1
Skewness-0.10517327
Sum4152
Variance1.5117211
MonotonicityNot monotonic
2024-01-15T00:44:02.131472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 435
29.6%
2 387
26.3%
4 311
21.2%
1 159
 
10.8%
5 134
 
9.1%
0 44
 
3.0%
ValueCountFrequency (%)
0 44
 
3.0%
1 159
 
10.8%
2 387
26.3%
3 435
29.6%
4 311
21.2%
5 134
 
9.1%
ValueCountFrequency (%)
5 134
 
9.1%
4 311
21.2%
3 435
29.6%
2 387
26.3%
1 159
 
10.8%
0 44
 
3.0%

Interactions

2024-01-15T00:43:52.175062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:23.930579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:25.527424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:27.236569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:28.922756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:31.263258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:32.967104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:34.724549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:36.543112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:38.127508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:39.805070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:41.744390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:43.418818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:45.132638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:46.835314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:48.837939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:50.563090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:52.261379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:24.017883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:25.623194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:27.328223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:29.012218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:31.359777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:33.062449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:34.990047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:36.630073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:38.219314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:39.896683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:41.830393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:43.514085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:45.228214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:46.937358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:48.932637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:50.649127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:52.375165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:24.113617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:25.734162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:27.426146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:29.107191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:31.465736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:33.164082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:35.084065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:36.722086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:38.315328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:40.000516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:41.930475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:43.613004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:45.333312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:47.039402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:49.042088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:50.744608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:52.469524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:24.212011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:25.833354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:27.524705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:29.907670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:31.566584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:33.262022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:35.179071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:36.812701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:38.414317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:40.103956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:42.028784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:43.713101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:45.435772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:47.141269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:49.139511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:50.841307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:52.555551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:24.297837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:25.928070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:27.615054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:30.005246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:31.658146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:33.358103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:35.275115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:36.897744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:38.503961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:40.200462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:42.120018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:43.806795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:45.527060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:47.240324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:49.234750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:50.929321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:52.652098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:24.393850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:26.036030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:27.717479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:30.106442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:31.758593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:33.471644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:35.379238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:36.995079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:38.625410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:40.304363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:42.223205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:43.909749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:24.954835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:30.684937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:41.121967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:42.932547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:44.629025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:48.087646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:51.804794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:32.675219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:37.847795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:50.265110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:51.899101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:53.623820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:25.348535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:28.829545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:32.870096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:34.625816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:41.649249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:43.327856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:45.037189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2024-01-15T00:43:50.467788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-01-15T00:43:52.085354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2024-01-15T00:44:02.245922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
AgeAttritionBusinessTravelDailyAchievementDepartmentDistanceFromHomeEducationEducationFieldEmployeeNumberEnvironmentSatisfactionGenderHourlyAchievementHowToEmployIncentiveJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyAchievementMonthlyIncomeNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionRemoteWorkStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
Age1.0000.2130.0410.0070.000-0.0190.1530.000-0.0020.0060.0000.0290.0640.0110.0250.2950.1750.0000.1410.0170.4720.3530.0000.0080.0000.0350.0060.0930.6570.0000.0330.2520.1980.1740.195
Attrition0.2131.0000.123-0.0570.0770.0790.0000.087-0.0100.1150.009-0.0070.070-0.0690.1320.2160.2310.0990.1730.015-0.1980.0310.243-0.0240.0350.039-0.1240.198-0.199-0.0520.095-0.190-0.181-0.053-0.175
BusinessTravel0.0410.1231.000-0.0000.000-0.0070.0000.000-0.0110.0000.0370.0280.024-0.0150.0160.0000.0000.0000.035-0.0120.0280.0340.024-0.0270.0290.000-0.0470.0000.0250.0150.000-0.022-0.025-0.035-0.024
DailyAchievement0.007-0.057-0.0001.0000.000-0.0030.0170.039-0.0520.0000.0310.0240.048-0.0120.0160.0000.0000.0000.085-0.0320.0160.0370.0000.0250.0220.0000.0090.0400.021-0.0110.012-0.0100.007-0.038-0.005
Department0.0000.0770.0000.0001.0000.0300.0000.588-0.0040.0180.026-0.0070.0280.0480.0000.2120.9370.0290.0300.0220.165-0.0310.000-0.0060.0000.0200.0260.000-0.0040.0430.0470.0340.0560.0200.024
DistanceFromHome-0.0190.079-0.007-0.0030.0301.0000.0000.0000.0390.0000.0300.0200.0240.0390.0280.0540.0000.0000.0000.0400.003-0.0100.0660.0300.0270.0250.4770.015-0.003-0.0250.0000.0110.014-0.0050.004
Education0.1530.0000.0000.0170.0000.0001.0000.0550.0430.0190.0000.0140.0000.0360.0000.0880.0510.0150.000-0.0210.1200.1350.0010.0040.0490.0160.0050.0270.162-0.0240.0000.0640.0550.0320.051
EducationField0.0000.0870.0000.0390.5880.0000.0551.000-0.0050.0310.000-0.0260.000-0.0040.0000.0910.3360.0170.000-0.028-0.035-0.0120.000-0.0020.0000.040-0.0270.032-0.0220.0490.027-0.001-0.0070.0130.008
EmployeeNumber-0.002-0.010-0.011-0.052-0.0040.0390.043-0.0051.0000.0000.0500.0350.0000.0320.0350.0360.0000.0000.0000.0120.0020.0070.016-0.0080.0570.0550.0100.068-0.0040.0270.0000.013-0.0010.008-0.005
EnvironmentSatisfaction0.0060.1150.0000.0000.0180.0000.0190.0310.0001.0000.000-0.0520.0000.0150.0340.0000.0000.0000.0190.037-0.0150.0060.060-0.0300.0000.0000.0040.000-0.014-0.0120.0000.0080.0200.026-0.002
Gender0.0000.0090.0370.0310.0260.0300.0000.0000.0500.0001.000-0.0000.000-0.0000.0000.0480.0740.0000.032-0.042-0.044-0.0410.0310.0100.0000.0000.0430.000-0.049-0.0330.000-0.042-0.030-0.025-0.027
HourlyAchievement0.029-0.0070.0280.024-0.0070.0200.014-0.0260.035-0.052-0.0001.0000.0260.0260.0000.0000.0230.0100.000-0.015-0.0200.0190.064-0.0100.0110.000-0.0350.052-0.0120.0000.000-0.029-0.034-0.052-0.014
HowToEmploy0.0640.0700.0240.0480.0280.0240.0000.0000.0000.0000.0000.0261.000-0.1080.0000.1060.0930.0000.024-0.063-0.068-0.0050.048-0.0010.1610.032-0.0170.003-0.055-0.0400.000-0.027-0.012-0.0430.006
Incentive0.011-0.069-0.015-0.0120.0480.0390.036-0.0040.0320.015-0.0000.026-0.1081.0000.0070.3070.1690.0210.0000.4060.0200.0510.0000.5350.3780.0540.2180.000-0.015-0.0060.000-0.043-0.005-0.055-0.023
JobInvolvement0.0250.1320.0160.0160.0000.0280.0000.0000.0350.0340.0000.0000.0000.0071.0000.0000.0000.0000.024-0.018-0.0250.0150.000-0.0170.0000.000-0.0050.0220.0060.0020.0000.0140.016-0.0080.037
JobLevel0.2950.2160.0000.0000.2120.0540.0880.0910.0360.0000.0480.0000.1060.3070.0001.0000.5690.0000.0460.0530.9200.1780.000-0.0320.0000.0000.0580.0690.735-0.0200.0000.4720.3910.2690.371
JobRole0.1750.2310.0000.0000.9370.0000.0510.3360.0000.0000.0740.0230.0930.1690.0000.5691.0000.0000.0610.006-0.044-0.0660.0000.0020.0000.0300.0110.039-0.1480.0220.029-0.055-0.013-0.019-0.035
JobSatisfaction0.0000.0990.0000.0000.0290.0000.0150.0170.0000.0000.0000.0100.0000.0210.0000.0000.0001.0000.000-0.0030.005-0.0520.0220.0240.0000.0000.4860.000-0.016-0.0120.0000.0120.0010.007-0.017
MaritalStatus0.1410.1730.0350.0850.0300.0000.0000.0000.0000.0190.0320.0000.0240.0000.0240.0460.0610.0001.0000.025-0.079-0.0530.0000.0140.0000.025-0.0220.581-0.0930.0110.000-0.071-0.065-0.018-0.047
MonthlyAchievement0.0170.015-0.012-0.0320.0220.040-0.021-0.0280.0120.037-0.042-0.015-0.0630.406-0.0180.0530.006-0.0030.0251.0000.0540.0200.000-0.0050.0660.0550.0200.0000.013-0.0100.034-0.030-0.007-0.016-0.035
MonthlyIncome0.472-0.1980.0280.0160.1650.0030.120-0.0350.002-0.015-0.044-0.020-0.0680.020-0.0250.920-0.0440.005-0.0790.0541.0000.1900.000-0.0340.0000.0430.0500.0560.710-0.0350.0000.4640.3950.2650.365
NumCompaniesWorked0.3530.0310.0340.037-0.031-0.0100.135-0.0120.0070.006-0.0410.019-0.0050.0510.0150.178-0.066-0.052-0.0530.0200.1901.0000.0000.0000.0430.000-0.0220.0000.315-0.0470.051-0.171-0.128-0.067-0.144
OverTime0.0000.2430.0240.0000.0000.0660.0010.0000.0160.0600.0310.0640.0480.0000.0000.0000.0000.0220.0000.0000.0000.0001.000-0.0150.0190.025-0.0230.0000.000-0.0710.000-0.036-0.036-0.016-0.040
PercentSalaryHike0.008-0.024-0.0270.025-0.0060.0300.004-0.002-0.008-0.0300.010-0.010-0.0010.535-0.017-0.0320.0020.0240.014-0.005-0.0340.000-0.0151.0000.7420.0270.3140.000-0.026-0.0040.000-0.054-0.026-0.055-0.026
PerformanceRating0.0000.0350.0290.0220.0000.0270.0490.0000.0570.0000.0000.0110.1610.3780.0000.0000.0000.0000.0000.0660.0000.0430.0190.7421.0000.0120.3280.0000.020-0.0070.019-0.0220.007-0.036-0.005
RelationshipSatisfaction0.0350.0390.0000.0000.0200.0250.0160.0400.0550.0000.0000.0000.0320.0540.0000.0000.0300.0000.0250.0550.0430.0000.0250.0270.0121.000-0.0400.0300.0040.0050.000-0.001-0.0210.0370.000
RemoteWork0.006-0.124-0.0470.0090.0260.4770.005-0.0270.0100.0040.043-0.035-0.0170.218-0.0050.0580.0110.486-0.0220.0200.050-0.022-0.0230.3140.328-0.0401.0000.0570.023-0.0380.0000.0240.0220.0250.018
StockOptionLevel0.0930.1980.0000.0400.0000.0150.0270.0320.0680.0000.0000.0520.0030.0000.0220.0690.0390.0000.5810.0000.0560.0000.0000.0000.0000.0300.0571.0000.0530.0030.0190.0650.0720.0280.054
TotalWorkingYears0.657-0.1990.0250.021-0.004-0.0030.162-0.022-0.004-0.014-0.049-0.012-0.055-0.0150.0060.735-0.148-0.016-0.0930.0130.7100.3150.000-0.0260.0200.0040.0230.0531.000-0.0140.0000.5940.4930.3350.495
TrainingTimesLastYear0.000-0.0520.015-0.0110.043-0.025-0.0240.0490.027-0.012-0.0330.000-0.040-0.0060.002-0.0200.022-0.0120.011-0.010-0.035-0.047-0.071-0.004-0.0070.005-0.0380.003-0.0141.0000.0000.0010.0050.010-0.012
WorkLifeBalance0.0330.0950.0000.0120.0470.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0340.0000.0510.0000.0000.0190.0000.0000.0190.0000.0001.0000.0050.0230.002-0.005
YearsAtCompany0.252-0.190-0.022-0.0100.0340.0110.064-0.0010.0130.008-0.042-0.029-0.027-0.0430.0140.472-0.0550.012-0.071-0.0300.464-0.171-0.036-0.054-0.022-0.0010.0240.0650.5940.0010.0051.0000.8540.5200.843
YearsInCurrentRole0.198-0.181-0.0250.0070.0560.0140.055-0.007-0.0010.020-0.030-0.034-0.012-0.0050.0160.391-0.0130.001-0.065-0.0070.395-0.128-0.036-0.0260.007-0.0210.0220.0720.4930.0050.0230.8541.0000.5060.725
YearsSinceLastPromotion0.174-0.053-0.035-0.0380.020-0.0050.0320.0130.0080.026-0.025-0.052-0.043-0.055-0.0080.269-0.0190.007-0.018-0.0160.265-0.067-0.016-0.055-0.0360.0370.0250.0280.3350.0100.0020.5200.5061.0000.467
YearsWithCurrManager0.195-0.175-0.024-0.0050.0240.0040.0510.008-0.005-0.002-0.027-0.0140.006-0.0230.0370.371-0.035-0.017-0.047-0.0350.365-0.144-0.040-0.026-0.0050.0000.0180.0540.495-0.012-0.0050.8430.7250.4671.000

Missing values

2024-01-15T00:43:53.907105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-15T00:43:54.586257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeAttritionBusinessTravelDailyAchievementDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyAchievementJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyAchievementNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerHowToEmployIncentiveRemoteWork
025NoTravel_Rarely1280Research & Development71Medical11434Male6421Research Scientist4Married2889268971YNo5138022232221intern04
127NoTravel_Rarely1167Research & Development42Life Sciences112591Male7631Research Scientist3Divorced251732081YNo5128035235303intern01
225YesTravel_Rarely240Sales53Marketing11423Male4622Sales Executive3Single5744269591YYes5148006136403agent_A02
328NoTravel_Rarely440Research & Development213Medical12213Male4231Research Scientist4Married271366721YNo5138015215202New_graduate_recruitment05
428YesTravel_Rarely529Research & Development24Life Sciences13641Male7931Laboratory Technician3Single3485149352YNo5138005510000New_graduate_recruitment01
527NoTravel_Rarely1377Sales23Life Sciences14374Male7432Sales Executive3Single447852421YYes5118005335404New_graduate_recruitment02
621YesTravel_Frequently756Sales11Technical Degree14781Female9921Sales Representative2Single217491501YYes5138003333212direct_recruting01
725NoTravel_Rarely891Sales42Life Sciences15272Female9922Sales Executive4Single4487120901YYes5128005335413agent_A03
826YesTravel_Frequently426Human Resources174Life Sciences16082Female5831Human Resources3Divorced2741228080YYes5128018227710agent_A02
924NoTravel_Rarely691Research & Development233Medical16392Male8941Research Scientist4Married2725216301YYes5128026336514New_graduate_recruitment04
AgeAttritionBusinessTravelDailyAchievementDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyAchievementJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyAchievementNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerHowToEmployIncentiveRemoteWork
146036NoTravel_Rarely1278Human Resources83Life Sciences18781Male7721Human Resources1Married234286350YNo15438006335403agent_A11711
146136NoTravel_Rarely216Research & Development62Medical11782Male8432Manufacturing Director2Divorced494128196YNo14448027033201New_graduate_recruitment04
146219NoTravel_Rarely1181Research & Development31Medical12012Female7931Laboratory Technician2Single1483161021YNo8348001331000New_graduate_recruitment7412
146328NoNon-Travel120Sales43Medical11292Male4332Sales Executive3Married422188631YNo9328005345404New_graduate_recruitment21104
146441NoTravel_Frequently1200Research & Development223Life Sciences113924Female7532Research Scientist4Divorced5467139533YYes83180212426233intern27335
146539NoTravel_Rarely1089Research & Development63Life Sciences115252Female3233Manufacturing Director2Single837691504YNo12348009332022New_graduate_recruitment7743
146632NoNon-Travel1146Research & Development154Medical119553Female3432Healthcare Representative4Divorced6667165425YNo12328019635112New_graduate_recruitment33335
146735NoTravel_Frequently853Sales185Life Sciences1742Male7133Sales Executive1Married9069110311YNo16448019329818agent_A19623
146828NoTravel_Rarely995Research & Development93Medical19303Female7731Research Scientist3Divorced237798345YNo12328016232222intern11883
146943NoTravel_Rarely930Research & Development63Medical114021Female7322Research Scientist3Single4081200031YYes831800203120718New_graduate_recruitment20403